Graph-Based Collective Lexical Selection for Statistical Machine Translation

Su, Jinsong; Xiong, Deyi; Huang, Shujian; Han, Xianpei; Yao, Junfeng

doi:10.18653/v1/d15-1145

Cited by 5 publications

(3 citation statements)

References 24 publications

(24 reference statements)

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“…The authors used it to specify the senses of the source words and integrate them as contextual features with a MaxEnt-based translation model for English-Portuguese MT. Similarly, Su et al (2015) built a large weighted graph model of both source and target word dependencies and integrated them as features to a SMT model. However, apart from the sense graph, WordNet provides also textual information such as sense definitions and examples, which should be useful for disambiguating senses, but were not used in the above studies.…”

Section: Related Workmentioning

confidence: 99%

Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering

Pu¹,

Pappas²,

Popescu-Belis³

2017

Proceedings of the Second Conference on Machine Translation

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Statistical machine translation (SMT) systems use local cues from n-gram translation and language models to select the translation of each source word. Such systems do not explicitly perform word sense disambiguation (WSD), although this would enable them to select translations depending on the hypothesized sense of each word. Previous attempts to constrain word translations based on the results of generic WSD systems have suffered from their limited accuracy. We demonstrate that WSD systems can be adapted to help SMT, thanks to three key achievements: (1) we consider a larger context for WSD than SMT can afford to consider; (2) we adapt the number of senses per word to the ones observed in the training data using clustering-based WSD with K-means; and (3) we initialize senseclustering with definitions or examples extracted from WordNet. Our WSD system is competitive, and in combination with a factored SMT system improves noun and verb translation from English to Chinese, Dutch, French, German, and Spanish.

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Section: Related Workmentioning

confidence: 99%

Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering

Pu¹,

Pappas²,

Popescu-Belis³

2017

Proceedings of the Second Conference on Machine Translation

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“…Several studies integrated sense information as features to SMT, either obtained from the sense graph provided by WordNet (Neale et al, 2016) or generated from both sides of word dependencies (Su et al, 2015). However, apart from the sense graph, WordNet provides also textual information such as sense definitions and examples, which should be useful for WSD, but were not used in the above studies.…”

Section: Related Workmentioning

confidence: 99%

Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation

Pappas

Henderson

et al. 2018

TACL

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This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introduce three adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant processes, and random walks, which are then applied to large word contexts represented in a low-rank space and evaluated on SemEval shared-task data. We then learn word vectors jointly with sense vectors defined by our best WSD method, within a state-of-the-art NMT system. We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones. This is demonstrated by translation on five language pairs. The improvements are above one BLEU point over strong NMT baselines, +4% accuracy over all ambiguous nouns and verbs, or +20% when scored manually over several challenging words.

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“…Besides, the word ambiguities (or homographs), which are spelt the same but have different meanings, reduces the performance of both the SMT [19,28] and NMT [24,29]. The word ambiguity problem forces MT systems to choose among several translation candidates representing different senses of a source word.…”

Section: Introductionmentioning

confidence: 99%

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Nguyen

Shin

et al. 2020

Applied Sciences

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Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.

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Graph-Based Collective Lexical Selection for Statistical Machine Translation

Cited by 5 publications

References 24 publications

Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering

Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering

Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

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